ConditionalSpectralCausalityResult.pair#

ConditionalSpectralCausalityResult.pair(effect)[source]#

The conditional measure cause -> effect across the grid.

Parameters:

effect (str) – One of effect_names.

Return type:

NDArray[double]

Returns:

The (n,) non-negative measure.

Raises:

SpecificationError – If the label is unknown.

Example

>>> import numpy as np
>>> from cultivars.multivariate.reduced_form.vector_autoregression import VAR
>>> y = np.random.default_rng(0).standard_normal((200, 3))
>>> full = VAR(y, order=1).fit()
>>> without = VAR(y[:, [0, 2]], order=1, names=("y1", "y3")).fit()
>>> given = ConditionalSpectralCausality(full, without, n_frequencies=16).compute()
>>> curve = given.pair("y1")
>>> curve.shape, bool(np.array_equal(curve, given.measure[:, 0]))
((16,), True)